Dialogue
Dialogue is an ordered exchange of related messages across successive turns between participants or an interactive system and its users.
AI Agents and Applications
A chat message history is an ordered collection of user and language-model messages representing a conversation.
- How it is used
- Provides prior exchanges as context so chatbots and agents can answer related follow-up questions.
- How deep it goes
- Builds a message-based RAG workflow and a checkpointed agent, then inspects restored state; only briefly addresses long-history compression.
- Examples
- Tests a Paestum follow-up about “they” and a Penzance weather follow-up about “the same town.”
- What you can do after
- Apply it via LangChain message history or LangGraph checkpoints to maintain and test conversational context across turns.
Effective Conversational AI
Chat history is the recorded sequence of messages exchanged during a user's conversational interactions.
- How it is used
- Uses recorded user–bot exchanges to diagnose frustrating interactions, measure understanding, and support handoff summaries.
- How deep it goes
- Details log sampling, exchange-level organization, and intent annotation; gives summary prerequisites without a transcript-system implementation.
- Examples
- Illustrates a repeated “yes” response, first-utterance sampling, and comparisons between annotated intent volumes and training examples.
- What you can do after
- Evaluate/understand chatbot failures and intent accuracy using sampled, human-annotated dialogues while recognizing when later turns matter.
Build AI-Enhanced Web Apps
An ordered record of messages exchanged during a conversation with a user or conversational system.
- How it is used
- Maintains visible exchanges and supplies earlier messages as context for follow-up queries.
- How deep it goes
- Works through interface, state, session retrieval, and conversation-memory patterns, while leaving storage integration to the developer.
- Examples
- Uses a scrollable chat interface, a LangChain.js follow-up query, and an interview assistant storing session messages in Redis.
- What you can do after
- Apply it via LangChain.js memory components to give follow-up queries earlier context; plan separate storage for persistent sessions.
Data Analysis with LLMs
A message history is an ordered collection of conversational messages representing an interaction with a chat model.
- How it is used
- Supplies current and prior messages as context for chat replies and records desired interactions for fine-tuning.
- How deep it goes
- Defines message lists, dictionaries, roles, and content, then shows a completion call and labeled training histories without examining model internals.
- Examples
- Uses a story request, ChatGPT follow-ups, and a movie review paired with the desired `neg` label.
- What you can do after
- Apply it via the OpenAI Python library to submit message histories, or build labeled histories for fine-tuning.
Starting Data Analytics with Generative AI and Python
An interaction pattern in which multiple successive user and assistant messages form a single contextual conversation.
- How it is used
- Carries earlier prompts and responses into API requests so follow-up questions have exchange context.
- How deep it goes
- Defines message roles and history, and notes that requests must supply context because past requests are not remembered.
- Examples
- Uses a Python API exchange about explaining resonance to children, followed by a question about where resonance is used.
- What you can do after
- Apply dialogue by sending a message array containing prior user and assistant turns with each API request.
Read how Starting Data Analytics with Generative AI and Python explains dialogue →
LLMs in Production
Chat history is the ordered record of messages exchanged during a conversational interaction.
- How it is used
- Supplies prior user prompts and assistant responses as context for new prompts in an interactive LLM application.
- How deep it goes
- Demonstrates storing and displaying turns in Streamlit, notes token limits, and points to summarization for compression.
- Examples
- Shows a Streamlit chatbot storing role-tagged messages in session state and displaying earlier turns.
- What you can do after
- Apply it via Streamlit by storing chat turns in session state and using the history to form a new prompt.
Build an AI Agent (From Scratch)
Conversation history is the accumulated sequence of prior messages retained for continuing an interaction with a language model.
- How it is used
- Maintains coherent interactions by resending prior messages or retrieving a session across agent runs.
- How deep it goes
- Implements message accumulation and session persistence, while identifying token growth as a cost and deferring efficient context management.
- Examples
- Demonstrates retaining a name across API calls and agent runs using a message list or session ID.
- What you can do after
- Apply continuity by resending prior messages or reusing a session ID across agent runs.
Read how Build an AI Agent (From Scratch) explains dialogue →
Grokking AI Applications
A record of earlier messages exchanged between a user and an agent or model.
- How it is used
- Preserves conversational context by forwarding earlier turns with each model request.
- How deep it goes
- Explains application-managed history and demonstrates list-based reinjection, without detailing storage or long-thread limitations.
- Examples
- Uses an Andrea name-recall exchange and a Python chat loop with history_lines.
- What you can do after
- Build it from scratch by appending turns and injecting the resulting history into successive prompts.
Building Reliable AI Systems
A record of the messages exchanged during a conversation.
- How it is used
- Retains session context so an agent can reuse user details instead of asking for them again.
- How deep it goes
- Describes lightweight session storage and gives a simple list-based implementation.
- Examples
- Uses support-chat details, troubleshooting context, and an order ID to illustrate reuse during a session.
- What you can do after
- Build it from scratch by appending messages to a list and returning the stored history.
Build Applications with Local AI Models on a Mac
The example chatbot's ordered exchange of messages between a user and an AI assistant.
- How it is used
- Demonstrates how a chatbot uses conversation history to respond across multiple turns.
- How deep it goes
- Explains that the script sends full history and runs in a loop, without detailing history storage or message construction.
- Examples
- Uses a chatbot exchange where the assistant recalls a name and recommends a beginner programming book.
- What you can do after
- Operate a looping chatbot and end the conversation by typing quit, exit, or bye.
Read how Build Applications with Local AI Models on a Mac explains dialogue →
Related concepts
- VectorStoreChatMemoryAdvisorused by · VectorStoreChatMemoryAdvisor is a Spring AI advisor that stores chat messages as embeddings in a vector store and retrieves relevant messages as conversational context.
- messagehas part · A message is a discrete unit of information or data represented, processed, or exchanged in a communication context.
- contextuses · Context is the surrounding information, state, or circumstances that condition how an entity, event, or task is interpreted or how a system behaves.
- conversational aiused by · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.
- prompthas part · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
- memoryused by · Memory is a storage resource or mechanism that retains data or computational state for access during computation or later use.
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